class_docstr: NCSN++ model, adapted from https://github.com/yang-song/score_sde and
  https://github.com/sp-uhh/sgmse repository
code: def forward(self, x, time_cond):
      # timestep/noise_level embedding; only for continuous training
      ori_T = x.shape[-1]
      x = self.pad_spec(x)
      modules = self.all_modules
      m_idx = 0
  
      # Convert real and imaginary parts of (x,y) into four channel dimensions
      x = torch.cat(
          (
              x[:, [0], :, :].real,
              x[:, [0], :, :].imag,
              x[:, [1], :, :].real,
              x[:, [1], :, :].imag,
          ),
          dim=1,
      )
  
      if self.embedding_type == "fourier":
          # Gaussian Fourier features embeddings.
          used_sigmas = time_cond
          temb = modules[m_idx](torch.log(used_sigmas))
          m_idx += 1
  
      else:
          raise ValueError(
              f"embedding type {self.embedding_type} unknown."
          )
  
      if self.conditional:
          temb = modules[m_idx](temb)
          m_idx += 1
          temb = modules[m_idx](self.act(temb))
          m_idx += 1
      else:
          temb = None
  
      if not self.centered:
          # If input data is in [0, 1]
          x = 2 * x - 1.0
  
      # Downsampling block
      input_pyramid = None
      if self.progressive_input != "none":
          input_pyramid = x
  
      # Input layer: Conv2d: 4ch -> 128ch
      hs = [modules[m_idx](x)]
      m_idx += 1
  
      # Down path in U-Net
      for i_level in range(self.num_resolutions):
          # Residual blocks for this resolution
          for i_block in range(self.num_res_blocks):
              h = modules[m_idx](hs[-1], temb)
              m_idx += 1
              # Attention layer (optional)
              if (
                  h.shape[-2] in self.attn_resolutions
              ):  # edit: check H dim (-2) not W dim (-1)
                  h = modules[m_idx](h)
                  m_idx += 1
              hs.append(h)
  
          # Downsampling
          if i_level != self.num_resolutions - 1:
              if self.resblock_type == "ddpm":
                  h = modules[m_idx](hs[-1])
                  m_idx += 1
              else:
                  h = modules[m_idx](hs[-1], temb)
                  m_idx += 1
  
              if (
                  self.progressive_input == "input_skip"
              ):  # Combine h with x
                  input_pyramid = self.pyramid_downsample(input_pyramid)
                  h = modules[m_idx](input_pyramid, h)
                  m_idx += 1
  
              elif self.progressive_input == "residual":
                  input_pyramid = modules[m_idx](input_pyramid)
                  m_idx += 1
                  if self.skip_rescale:
                      input_pyramid = (input_pyramid + h) / np.sqrt(2.0)
                  else:
                      input_pyramid = input_pyramid + h
                  h = input_pyramid
              hs.append(h)
  
      h = hs[-1]  # actualy equal to: h = h
      h = modules[m_idx](h, temb)  # ResNet block
      m_idx += 1
      h = modules[m_idx](h)  # Attention block
      m_idx += 1
      h = modules[m_idx](h, temb)  # ResNet block
      m_idx += 1
  
      pyramid = None
  
      # Upsampling block
      for i_level in reversed(range(self.num_resolutions)):
          for i_block in range(self.num_res_blocks + 1):
              h = modules[m_idx](torch.cat([h, hs.pop()], dim=1), temb)
              m_idx += 1
  
          # edit: from -1 to -2
          if h.shape[-2] in self.attn_resolutions:
              h = modules[m_idx](h)
              m_idx += 1
  
          if self.progressive != "none":
              if i_level == self.num_resolutions - 1:
                  if self.progressive == "output_skip":
                      pyramid = self.act(modules[m_idx](h))  # GroupNorm
                      m_idx += 1
                      pyramid = modules[m_idx](
                          pyramid
                      )  # Conv2D: 256 -> 4
                      m_idx += 1
                  elif self.progressive == "residual":
                      pyramid = self.act(modules[m_idx](h))
                      m_idx += 1
                      pyramid = modules[m_idx](pyramid)
                      m_idx += 1
                  else:
                      raise ValueError(
                          f"{self.progressive} is not a valid name."
                      )
              else:
                  if self.progressive == "output_skip":
                      pyramid = self.pyramid_upsample(
                          pyramid
                      )  # Upsample
                      pyramid_h = self.act(
                          modules[m_idx](h)
                      )  # GroupNorm
                      m_idx += 1
                      pyramid_h = modules[m_idx](pyramid_h)
                      m_idx += 1
                      pyramid = pyramid + pyramid_h
                  elif self.progressive == "residual":
                      pyramid = modules[m_idx](pyramid)
                      m_idx += 1
                      if self.skip_rescale:
                          pyramid = (pyramid + h) / np.sqrt(2.0)
                      else:
                          pyramid = pyramid + h
                      h = pyramid
                  else:
                      raise ValueError(
                          f"{self.progressive} is not a valid name"
                      )
  
          # Upsampling Layer
          if i_level != 0:
              if self.resblock_type == "ddpm":
                  h = modules[m_idx](h)
                  m_idx += 1
              else:
                  h = modules[m_idx](h, temb)  # Upspampling
                  m_idx += 1
  
      assert not hs
  
      if self.progressive == "output_skip":
          h = pyramid
      else:
          h = self.act(modules[m_idx](h))
          m_idx += 1
          h = modules[m_idx](h)
          m_idx += 1
  
      assert m_idx == len(modules), "Implementation error"
      if self.scale_by_sigma:
          used_sigmas = used_sigmas.reshape(
              (x.shape[0], *([1] * len(x.shape[1:])))
          )
          h = h / used_sigmas
  
      # Convert back to complex number
      h = self.output_layer(h)
      h = torch.permute(h, (0, 2, 3, 1)).contiguous()
      h = torch.view_as_complex(h)[:, None, :, :]
      h = h[:, :, :, :ori_T]
      return h
  
file_path: espnet/espnet2/enh/layers/ncsnpp.py
imports: import functools
  import numpy
  import torch
  import torch.nn
  from espnet2.enh.layers.ncsnpp_utils import layers, layerspp, normalization
label_desc: Use image node_id 3 for calling the NCSNpp obj's underlying member method code with example usage: obj.forward(x, time_cond) and returns: h